{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import rasterio\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from geopandas import GeoSeries\n",
    "import os\n",
    "import h5py\n",
    "from shapely.geometry import Point,Polygon\n",
    "import glob\n",
    "from rasterio import features\n",
    "from topolib import gda_lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ATL06_list = glob.glob('/home/jovyan/data/gm_noCoverage_ICESat2/*.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ATL06_fn = '/home/jovyan/data/gm_noCoverage_ICESat2/processed_ATL06_20181110092841_06530106_001_01.h5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# read a Raster Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dem_fn = '/home/jovyan/data/USCOGM20160604f1a1_bareDEM_3p0m.tif'\n",
    "ds = rasterio.open(dem_fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# read a ATL06 hdf5 into a geodataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dict={'land_ice_segments':['h_li', 'delta_time','longitude','latitude'], 'land_ice_segments/ground_track':['x_atc']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ATL06_gdf = gda_lib.ATL06_2_gdf(ATL06_fn,dataset_dict)\n",
    "ATL06_gdf = ATL06_gdf.to_crs(ds.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dem = ds.read(1)\n",
    "dem = np.ma.masked_equal(dem,gda_lib.get_ndv(ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin,ymin,xmax,ymax = ds.bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f578d93d550>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "im = ax.imshow(dem,cmap='inferno',extent=[xmin,xmax,ymin,ymax])\n",
    "plt.colorbar(im,label='HAE (m WGS84)')\n",
    "ATL06_gdf.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ATL06_gdf_list = [gda_lib.ATL06_2_gdf(x,dataset_dict) for x in ATL06_list]\n",
    "ATL06_gdf_list = [x.to_crs(ds.crs) for x in ATL06_gdf_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "im = ax.imshow(dem,cmap='inferno',extent=[xmin,xmax,ymin,ymax])\n",
    "plt.colorbar(im,label='HAE (m WGS84)')\n",
    "for gdf in ATL06_gdf_list:\n",
    "    gdf.plot(ax=ax)\n",
    "#ATL06_gdf.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_atc,z = sample_values(ds,ATL06_gdf_list[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_atc = np.ma.array(x_atc,mask = z.mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e87febb402a246569799dbd42cfcd0e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9f1b5fe390>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.scatter(x_atc,z)\n",
    "ax.scatter(ATL06_gdf_list[6].x_atc.values,ATL06_gdf_list[6].h_li.values,marker='^',c='red',s=1)\n",
    "#im = ax.imshow(dem,cmap='inferno',extent=[xmin,xmax,ymin,ymax])\n",
    "#plt.colorbar(im,label='HAE (m WGS84)')\n",
    "#for gdf in ATL06_gdf_list:\n",
    " #   gdf.plot(ax=ax)\n",
    "#ATL06_gdf.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1,df2,df3,df4,df5,df6 = [x for x_,x in ATL06_gdf_list[6].groupby(ATL06_gdf_list[6]['p_b'])] #pick 1 ATL06 and seperate it into 6 beams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list = [df1,df2,df3,df4,df5,df6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Nearest neighbour sampling')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(2,3,figsize=(12,8))\n",
    "for i,ax in enumerate(fig.axes):\n",
    "    x_atc,z_hp = gda_lib.sample_near_nbor(ds,df_list[i])\n",
    "    ax.scatter(x_atc,z_hp,label='DEM Elevation')\n",
    "    ax.scatter(df_list[i].x_atc.values,df_list[i].h_li.values,marker='^',c='red',s=1,label='ATL06') #this is nearest neighbour sampling\n",
    "    ax.legend()   \n",
    "plt.suptitle('Nearest neighbour sampling')\n",
    "#ax.scatter(gdf_6_p1b1.x_atc.values,gdf_6_p1b1.h_li.values,marker='^',c='red',s=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Buffer sampling with window radius of 20 m')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(2,3,figsize=(12,8))\n",
    "for i,ax in enumerate(fig.axes):\n",
    "    x_atc,z_hp = gda_lib.buffer_sampler(ds,df_list[i],20) #this is buffer sampling\n",
    "    ax.scatter(x_atc,z_hp,label='DEM Elevation')\n",
    "    ax.scatter(df_list[i].x_atc.values,df_list[i].h_li.values,marker='^',c='red',s=1,label='ATL06') \n",
    "    ax.legend()   \n",
    "plt.suptitle('Buffer sampling with window radius of 20 m')\n",
    "#ax.scatter(gdf_6_p1b1.x_atc.values,gdf_6_p1b1.h_li.values,marker='^',c='red',s=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#not important from here on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mask_by_shp(geom,array,ds,reverse=False): \n",
    "    if (type(ds) == rasterio.io.DatasetReader):\n",
    "        transform = ds.transform\n",
    "    else:\n",
    "        transform = affine.Affine.from_gdal(*ds.GetGeoTransform())\n",
    "    shp = features.rasterize(geom,out_shape=np.shape(array),fill=-9999,transform=transform,dtype=float)\n",
    "    shp_mask = np.ma.masked_where(shp==-9999,shp)\n",
    "    if reverse:\n",
    "        req_mask = ~shp_mask.mask\n",
    "    else:\n",
    "        req_mask = shp_mask.mask\n",
    "    masked_array = np.ma.array(array,mask=req_mask)\n",
    "    return masked_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def median_profile(ds,geom,buff,val='med'):\n",
    "    \"\"\"\n",
    "    sample values from raster at the given ICESat-2 points\n",
    "    using a buffer distance, and return median/mean\n",
    "    \"\"\"\n",
    "    # reproject the shapefile to raster projection\n",
    "    x_min,y_min,x_max,y_max = ds.bounds\n",
    "    geom = geom.to_crs(ds.crs)\n",
    "    #filter geom outside bounds \n",
    "    geom = geom.cx[x_min:x_max,y_min:y_max]\n",
    "    #adjust for no data\n",
    "    no_data = get_ndv(ds)\n",
    "    array = ds.read(1)\n",
    "    array = np.ma.masked_equal(array,no_data)\n",
    "    geom['geometry'] = geom.geometry.buffer(buff)\n",
    "   # print(type(geom))\n",
    "    out_list = []\n",
    "    for i,row in geom.iterrows():\n",
    "       # print(type(row))\n",
    "        poly_array =  mask_by_shp(GeoSeries(row.geometry),array,ds)\n",
    "        if val == 'med':\n",
    "            out = np.ma.median(poly_array)\n",
    "        else:\n",
    "            out = np.ma.mean(poly_array)\n",
    "        out_list.append(out)\n",
    "    return geom.x_atc.values, out_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = ds.read(1,masked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-54-40156ade5297>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmedian_profile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-53-b20005514e39>\u001b[0m in \u001b[0;36mmedian_profile\u001b[0;34m(ds, geom, buff, val)\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mpoly_array\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mmask_by_shp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGeoSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeometry\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mval\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'med'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoly_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoly_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/lib/python3.6/site-packages/numpy/ma/extras.py\u001b[0m in \u001b[0;36mmedian\u001b[0;34m(a, axis, out, overwrite_input, keepdims)\u001b[0m\n\u001b[1;32m    692\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m     r, k = _ureduce(a, func=_median, axis=axis, out=out,\n\u001b[0;32m--> 694\u001b[0;31m                     overwrite_input=overwrite_input)\n\u001b[0m\u001b[1;32m    695\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    696\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/lib/python3.6/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_ureduce\u001b[0;34m(a, func, **kwargs)\u001b[0m\n\u001b[1;32m   3403\u001b[0m         \u001b[0mkeepdim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3405\u001b[0;31m     \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3406\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3407\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/lib/python3.6/site-packages/numpy/ma/extras.py\u001b[0m in \u001b[0;36m_median\u001b[0;34m(a, axis, out, overwrite_input)\u001b[0m\n\u001b[1;32m    713\u001b[0m             \u001b[0masorted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 715\u001b[0;31m         \u001b[0masorted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    716\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    717\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/lib/python3.6/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36msort\u001b[0;34m(a, axis, kind, order, endwith, fill_value)\u001b[0m\n\u001b[1;32m   6709\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6710\u001b[0m         a.sort(axis=axis, kind=kind, order=order,\n\u001b[0;32m-> 6711\u001b[0;31m                endwith=endwith, fill_value=fill_value)\n\u001b[0m\u001b[1;32m   6712\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6713\u001b[0m         \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/lib/python3.6/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36msort\u001b[0;34m(self, axis, kind, order, endwith, fill_value)\u001b[0m\n\u001b[1;32m   5559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5560\u001b[0m         sidx = self.argsort(axis=axis, kind=kind, order=order,\n\u001b[0;32m-> 5561\u001b[0;31m                             fill_value=fill_value, endwith=endwith)\n\u001b[0m\u001b[1;32m   5562\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5563\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake_along_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msidx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/lib/python3.6/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36margsort\u001b[0;34m(self, axis, kind, order, endwith, fill_value)\u001b[0m\n\u001b[1;32m   5406\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5407\u001b[0m         \u001b[0mfilled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5408\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfilled\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5410\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0margmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "median_profile(ds,df_list[2],20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.nodata ==None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'driver': 'GTiff', 'dtype': 'float32', 'nodata': None, 'width': 9955, 'height': 5857, 'count': 1, 'crs': CRS.from_epsg(32612), 'transform': Affine(3.0, 0.0, 746619.0,\n",
       "       0.0, -3.0, 4338012.0), 'tiled': False, 'interleave': 'band'}"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gdal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "band = gdal.Open(dem_fn,2).GetRasterBand(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<osgeo.gdal.Band; proxy of <Swig Object of type 'GDALRasterBandShadow *' at 0x7fba046ed4e0> >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "band.GetNoDataValue()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'band' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-3d48d74b448b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mband\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetNoDataValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'band' is not defined"
     ]
    }
   ],
   "source": [
    "band.GetNoDataValue()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ndv(ds):\n",
    "    no_data = ds.nodatavals[0]\n",
    "    if no_data == None:\n",
    "        #this means no data is not set in tif tag, nead to cheat it from raster\n",
    "        ndv = ds.read(1)[0,0]\n",
    "    else:\n",
    "        ndv = no_data\n",
    "    return ndv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(\n",
       "  data=[[--, --, --, ..., --, --, --],\n",
       "        [--, --, --, ..., --, --, --],\n",
       "        [--, --, --, ..., --, --, --],\n",
       "        ...,\n",
       "        [--, --, --, ..., --, --, --],\n",
       "        [--, --, --, ..., --, --, --],\n",
       "        [--, --, --, ..., --, --, --]],\n",
       "  mask=[[ True,  True,  True, ...,  True,  True,  True],\n",
       "        [ True,  True,  True, ...,  True,  True,  True],\n",
       "        [ True,  True,  True, ...,  True,  True,  True],\n",
       "        ...,\n",
       "        [ True,  True,  True, ...,  True,  True,  True],\n",
       "        [ True,  True,  True, ...,  True,  True,  True],\n",
       "        [ True,  True,  True, ...,  True,  True,  True]],\n",
       "  fill_value=1e+20,\n",
       "  dtype=float32)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_buffered = df_list[5].copy()\n",
    "df_buffered['geometry'] = df_buffered.geometry.buffer(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "dem_filled = np.ma.filled(dem,np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem_filled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterstats as rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_stats = gpd.GeoDataFrame.from_features(rs.zonal_stats(df_buffered,dem,affine=ds.transform,geojson_out=True,stats=\"median\",nodata=-9999))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_stats = profile_stats.rename(columns={'median':'med'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beam</th>\n",
       "      <th>delta_time</th>\n",
       "      <th>filename</th>\n",
       "      <th>geometry</th>\n",
       "      <th>h_li</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>med</th>\n",
       "      <th>p_b</th>\n",
       "      <th>pair</th>\n",
       "      <th>x_atc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.992328e+07</td>\n",
       "      <td>/home/jovyan/data/gm_noCoverage_ICESat2/proces...</td>\n",
       "      <td>POLYGON ((752773.3263820586 4354161.339010223,...</td>\n",
       "      <td>2109.107178</td>\n",
       "      <td>39.299962</td>\n",
       "      <td>-108.068931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0_1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.569845e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.992328e+07</td>\n",
       "      <td>/home/jovyan/data/gm_noCoverage_ICESat2/proces...</td>\n",
       "      <td>POLYGON ((752771.9874215935 4354141.33305324, ...</td>\n",
       "      <td>2103.466797</td>\n",
       "      <td>39.299782</td>\n",
       "      <td>-108.068954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0_1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.569847e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.992328e+07</td>\n",
       "      <td>/home/jovyan/data/gm_noCoverage_ICESat2/proces...</td>\n",
       "      <td>POLYGON ((752770.6514802576 4354121.325010256,...</td>\n",
       "      <td>2094.860352</td>\n",
       "      <td>39.299603</td>\n",
       "      <td>-108.068977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0_1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.569849e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.992328e+07</td>\n",
       "      <td>/home/jovyan/data/gm_noCoverage_ICESat2/proces...</td>\n",
       "      <td>POLYGON ((752769.3620773242 4354101.314930591,...</td>\n",
       "      <td>2086.094971</td>\n",
       "      <td>39.299423</td>\n",
       "      <td>-108.068999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0_1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.569851e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.992328e+07</td>\n",
       "      <td>/home/jovyan/data/gm_noCoverage_ICESat2/proces...</td>\n",
       "      <td>POLYGON ((752768.0751188451 4354081.304702571,...</td>\n",
       "      <td>2074.346680</td>\n",
       "      <td>39.299243</td>\n",
       "      <td>-108.069021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0_1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.569853e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beam    delta_time                                           filename  \\\n",
       "0   1.0  2.992328e+07  /home/jovyan/data/gm_noCoverage_ICESat2/proces...   \n",
       "1   1.0  2.992328e+07  /home/jovyan/data/gm_noCoverage_ICESat2/proces...   \n",
       "2   1.0  2.992328e+07  /home/jovyan/data/gm_noCoverage_ICESat2/proces...   \n",
       "3   1.0  2.992328e+07  /home/jovyan/data/gm_noCoverage_ICESat2/proces...   \n",
       "4   1.0  2.992328e+07  /home/jovyan/data/gm_noCoverage_ICESat2/proces...   \n",
       "\n",
       "                                            geometry         h_li   latitude  \\\n",
       "0  POLYGON ((752773.3263820586 4354161.339010223,...  2109.107178  39.299962   \n",
       "1  POLYGON ((752771.9874215935 4354141.33305324, ...  2103.466797  39.299782   \n",
       "2  POLYGON ((752770.6514802576 4354121.325010256,...  2094.860352  39.299603   \n",
       "3  POLYGON ((752769.3620773242 4354101.314930591,...  2086.094971  39.299423   \n",
       "4  POLYGON ((752768.0751188451 4354081.304702571,...  2074.346680  39.299243   \n",
       "\n",
       "    longitude  med      p_b  pair         x_atc  \n",
       "0 -108.068931  NaN  3.0_1.0   3.0  1.569845e+07  \n",
       "1 -108.068954  NaN  3.0_1.0   3.0  1.569847e+07  \n",
       "2 -108.068977  NaN  3.0_1.0   3.0  1.569849e+07  \n",
       "3 -108.068999  NaN  3.0_1.0   3.0  1.569851e+07  \n",
       "4 -108.069021  NaN  3.0_1.0   3.0  1.569853e+07  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2ae0375224245edbcd23b9854c3a519",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb9a8c915f8>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.scatter(profile_stats.x_atc.values,profile_stats.med.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "def buffer_sampler(ds,geom,buffer,val='median',ret_gdf=False):\n",
    "    \"\"\"\n",
    "    sample values from raster at the given ICESat-2 points\n",
    "    using a buffer distance, and return median/mean or a full gdf ( if return gdf=True)\n",
    "    Inputs = rasterio dataset, Geodataframe containing points, buffer distance, output value = median/mean (default median)\n",
    "    and output format list of x_atc,output_value arrays (default) or  full gdf\n",
    "    \"\"\"\n",
    "    ndv = get_ndv(ds)\n",
    "    array = ds.read(1)\n",
    "    gt = ds.transform\n",
    "    stat = val\n",
    "    geom = geom.to_crs(ds.crs)\n",
    "    geom['geometry'] = geom.geometry.buffer(buffer)\n",
    "    json_stats = rs.zonal_stats(geom,array,affine=gt,geojson_out=True,stats=stat,nodata=ndv)\n",
    "    gdf = gpd.GeoDataFrame.from_features(json_stats)\n",
    "    if val =='median':\n",
    "        gdf = gdf.rename(columns={'median':'med'})\n",
    "        call = 'med'\n",
    "    else:\n",
    "        gdf = gdf.rename(columns={'mean':'avg'})\n",
    "        call = 'avg'\n",
    "    if ret_gdf:\n",
    "        out_file = gdf\n",
    "    else:\n",
    "        out_file = [gdf.x_atc.values,gdf[call].values]\n",
    "    return out_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from topolib import gda_lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y = gda_lib.buffer_sampler(ds,df_list[5],20,'mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/topohack/ShashankBice\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
